Exam Notes

Google Cloud and Assignment 3

• Trends putting pressure on conventional computing centers:
  • Explosive growth in applications.
  • Extreme scale content generation.
  • Extraordinary rate of digital content consumption.
  • Exponential growth in compute capabilities.
  • Very short cycle of obsolescence in technologies.
  • Newer architectures.
• Cloud Computing Definition:
  • Using a network of remote servers to store, manage, and process data.
  • Internet-based computing providing shared resources on-demand, like the electricity grid.
• Other names for cloud computing: On-demand computing, utility computing, ubiquitous computing, autonomic computing, platform computing, edge computing, elastic computing, grid computing
• Platform: Integrated and networked hardware, software, and Internet infrastructure.
• Cloud Computing platform functions:
  • Provides hardware, software, and networking services via the Internet.
  • Hides complexity of underlying infrastructure with simple interface/API.
  • Offers on-demand, 24/7 services.
  • Pay-as-you-go, elastic services.
  • Hardware and software services available to everyone.
• Benefits of cloud computing:
  • Saves capital and operational investment with pay-as-you-go model.
  • Enables companies to be infrastructure-less.
• Virtualization: Creation of a virtual version of something (OS, server, etc.).
• Advantages of virtual machines:
  • Run OS where hardware is unavailable.
  • Easier to create and backup machines.
  • Software testing with clean OS installs.
  • Emulate more machines than physically available.
  • Timeshare lightly loaded systems.
  • Debug problems (suspend and resume).
  • Easy migration.
  • Run legacy systems.
• Hypervisor/VMM: Software, firmware, or hardware that creates and runs virtual machines.
• Host vs. Guest Machine:
  • Host: Computer running the hypervisor.
  • Guest: Each virtual machine.
• Hypervisor Vendors: VMware ESX Server, Microsoft Windows Hyper-V, Oracle VM Virtual Box, Xen Project
• Virtual Workspace: Abstraction of an execution environment dynamically available to clients.
• Full-virtualization vs. Para-virtualization:
  • Full: Runs an unmodified guest OS.
  • Para: Modifies the guest OS for performance gains, requires modified kernels.
• Top-5 cloud-computing vendors: Microsoft, Amazon, IBM, Oracle, Google Cloud, Alibaba

Map/Reduce

• MapReduce: Methodology for exploiting parallelism in computing clouds.
• Developed by Google to process large amounts of data quickly.
• Motivation: Need to manage immense data quickly, exploit parallelism in regular data.
• Examples beyond search: Dish network (click data), Tesla (car usage).
• Search engine uses: index building, article clustering, spam detection
• Apache Hadoop: Open-source implementation of MapReduce in Java for distributed storage and processing on commodity hardware.
• Hadoop parts: Hadoop Distributed File System (HDFS) and MapReduce.
• GFS, HDFS, and CloudStore:
  • Common: Distributed files, rarely updated, often read/appended, divided into chunks and replicated.
  • GFS: Google File System.
  • HDFS: written in Java.
  • CloudStore: C++ implementation of GFS.
• Parallelization issues:
  • Assigning work units to worker threads.
  • Handling more work units than threads.
  • Aggregating/combining results.
  • Knowing when all workers have finished.
  • Dividing work into separate tasks.
• How MapReduce solves parallelization issues:
  • Automatic parallelization & distribution.
  • Fault tolerance.
  • I/O scheduling.
  • Monitoring & status updates.
• Typical MapReduce Architecture:
  • Racks of CPUs with 16-64 nodes.
  • Nodes connected by gigabyte Ethernet within a rack.
  • Racks connected by a switch (2-10 Gbps).
• Cluster Computing: Collection of compute nodes on racks connected by switches.
• Inter-rack bandwidth is generally faster than intra-rack bandwidth.
• Dealing with failure in DFS:
  • Redundant file storage.
  • Computations divided into restartable tasks.
  • Constant machine pinging.
• MapReduce is a programming model borrowed from Lisp.
• Paradigm of Map/Reduce:
  • Records are broken into segments
  • Map: extract something of interest from each segment
  • Group and sort intermediate results from each segment
  • Reduce: aggregate intermediate results
  • Generate final output
• To use MapReduce, you must write Map and Reduce functions.
• MapReduce computation process:
  • Map tasks process chunks into key-value pairs.
  • Master controller collects pairs, sorts by key, divides among Reduce tasks
  • Reduce tasks combine values for each key.
• Master controller in MapReduce:
  • Knows the number of Reduce tasks (r).
  • Picks a hash function, assigns keys to buckets (0 to r-1).
  • Merges files from Map tasks for each Reduce task, feeds to process.
• Reduce function:
  • Takes key and list of values, combines them, generally associative and commutative.
  • The Reduce function output is a sequence of key-value pairs consisting of each input key k paired with the combined value.
  • Outputs from all Reduce tasks are merged into a single file.
• MapReduce Process:
  • User program forks a Master controller and Worker processes.
  • Worker handles Map/Reduce tasks.
• States of a map or reduce task: idle, executing, completed.
• Map task puts files for reduce task on local disk of Worker; Master knows locations.
• Fault tolerance strategy of MapReduce:
  • Task crashes: retry on another node.
  • Repeated failures: fail job or ignore input block.
  • Node crashes: relaunch tasks on other nodes.
  • Slow task (straggler): launch second copy.
• Coping with node failure in MapReduce:
  • Master fails: restart entire job.
  • Map worker fails: Master sets status to idle, re-schedules, informs Reduce tasks.
  • Reduce worker fails: Master sets status to idle, re-schedules.
• Example uses of MapReduce: distributed grep, distributed sort, web link-graph reversal, web access log stats, inverted index construction, document clustering, statistical machine translation

Search Engine Advertising

• Earliest form of online ads: Banner Advertising
• Types of online advertising: Banner Advertising, Pay-per-click Advertising, Website Advertising, Affiliate Marketing, Social Media Marketing
• Search Engine Advertising Revenue:
  • Google earned 100 billion in 2017
  • Yahoo earned 4.6 billion in 2017
  • Bing earned 1.8 billion in 2017
• Organic search results produced by algorithm, no guarantees of high ranking.
• Search engine optimizers(SEO) focus on developing and refining a company’s online presence
• Search engine optimization involves:
  • Making pages show up higher in search engine’s organic results
  • Optimizing content to target certain keyword phrases
  • Developing web page content that responds to each seeker’s interests
• Many search engines use pay-per-click (PPC) model for advertising.
• Adwords: Google's program for pay-per-click ads with keyword auction.
• Mapping keyword phrases on website:
  • Use Key Phrases in the content on your page
  • Develop meta data with Key Phrases
  • Name directories, files and images with the same key words or phrases
• Keyword matching options in Adwords: Broad Match, Exact Match, Phrase Match, Negative Keyword Match
• Default keyword matching in Adwords: Broad matching.
• Broad match features:
  • Results will show for expanded matches including synonyms and plurals, related terms and variations even if you don’t include these terms in your keyword list.
• Disadvantage of broad match:
  • Often less targeted than exact or phrase matches.
• Exact match rule in Adwords:
  • The search query must exactly match your keyword, but Google allows rewording and reordering, ignoring function words, conjunctions, articles and other words that don’t impact the intent of the query
• Phrase Match rule in Adwords:
  • Your ad appears when users search on the exact phrase, and when their search contains additional terms, as long as the keyword phrase is in exactly the same order.
• Negative keyword rule in Adwords:
  • Negative keywords allow you to eliminate searches that you know are not related to your message.
• Ad Server Functionality:
  • Basic: Uploading creatives, maintaining business rules, targeting ads, optimizing appearance.
  • Advanced: Frequency capping, sequencing, excluding competitors, roadblocks, behavioral targeting.
• Advertisers can define criteria in Google AdWords, Like time display: from 9:00AM-5:00PM EST, once/day
• Bidders specify:
  • Search terms that trigger bid
  • Bid amount for each term
  • Overall ad budget, bid limits
• How Google ranks bidders in AdWords:
  • Estimates click-through rate.
  • Ads with low click-through rates are not displayed.
  • Ranks by multiplying click-through-rate and bid amount.
• Google is paid only when an ad gets clicked. the price it receives is the smallest price the bidder could have bid to get its ranking
• Position of advertising in AdWords determined by Ad Rank.
  • Ad rank = Bid * Click Probability
• Determination of actual cost per click (CPC) of ads in AdWords:
  • Determined by ad rank of the next highest ad below you
  • Exception: if your the only bidder or lowest bid, you pay your maximum bid
  • CPC = The ad rank of the person below you / Your Quality Score + $0.01
• Click through rates influence bidders in AdWords; AdWords penalizes advertisers with low quality scores and Conversely, those with high Quality Scores get higher ad ranks and lower CPC
• Average cost per click on AdWords: roughly 2.32 on the search network and 0.58 on the display network
• Probability of clicking thorough may depend on :Historical click performance of the ad, Landing page quality, Relevance to the user, User click through rates
• Landing pages are the pages that appear when a user clicks on your ad
• AdSense from Google is a service for placing Google ads on web pages.
• Kinds of ads Google offer in AdSense:
  • Cost per Thousand displays, CPM, Cost per Engagement,
• Google will assign